
"""simple docstring"""



import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_ta import TaTokenizer
else:
 a       :						int       		=		None


a       :						int       		=		logging.get_logger(__name__)

a       :						Dict       		=		{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

a       :						Optional[Any]       		=		{
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/tokenizer.json''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/tokenizer.json''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/tokenizer.json''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/tokenizer.json''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/tokenizer.json''',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
a       :						Any       		=		{
    '''t5-small''': 5_1_2,
    '''t5-base''': 5_1_2,
    '''t5-large''': 5_1_2,
    '''t5-3b''': 5_1_2,
    '''t5-11b''': 5_1_2,
}




class 		a_    (			_UpperCAmelCase						):
    a   :					Any			     =					VOCAB_FILES_NAMES
    a   :					List[Any]			     =					PRETRAINED_VOCAB_FILES_MAP
    a   :					List[Any]			     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a   :					Optional[int]			     =					['input_ids', 'attention_mask']
    a   :					Optional[int]			     =					TaTokenizer

    a   :					List[int]			     =					[]







    def __init__(      self      :							int  ,       __UpperCamelCase      :							int=None  ,       __UpperCamelCase      :							List[str]=None  ,       __UpperCamelCase      :							List[str]="</s>"  ,       __UpperCamelCase      :							str="<unk>"  ,       __UpperCamelCase      :							List[Any]="<pad>"  ,       __UpperCamelCase      :							List[Any]=1_00  ,       __UpperCamelCase      :							Optional[Any]=None  ,       **__UpperCamelCase      :							Optional[Any]  ,       )		->List[str]:






        '''simple docstring'''






        if extra_ids > 0 and additional_special_tokens is None:
            _UpperCAmelCase        = [f"""<extra_id_{i}>""" for i in range(__UpperCamelCase  )]
        elif extra_ids > 0 and additional_special_tokens is not None:
            # Check that we have the right number of extra special tokens
            _UpperCAmelCase        = len(set(filter(lambda __UpperCamelCase  : bool("""extra_id_""" in str(__UpperCamelCase  )  )  ,       __UpperCamelCase  )  )  )
            if extra_tokens != extra_ids:
                raise ValueError(
                    f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
                    """ provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
                    """ tokens"""  )

        super().__init__(
            __UpperCamelCase  ,       tokenizer_file=__UpperCamelCase  ,       eos_token=__UpperCamelCase  ,       unk_token=__UpperCamelCase  ,       pad_token=__UpperCamelCase  ,       extra_ids=__UpperCamelCase  ,       additional_special_tokens=__UpperCamelCase  ,       **__UpperCamelCase  ,       )

        _UpperCAmelCase        = vocab_file
        _UpperCAmelCase        = False if not self.vocab_file else True
        _UpperCAmelCase        = extra_ids







    @staticmethod
    def   _snake_case       (      __UpperCamelCase      :							str  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							str  )		->Any:






        '''simple docstring'''






        if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
            _UpperCAmelCase        = TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
            if init_max_model_length is not None and init_max_model_length != max_model_length:
                return init_max_model_length
            elif init_max_model_length is None:
                warnings.warn(
                    """This tokenizer was incorrectly instantiated with a model max length of"""
                    f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
                    """ behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
                    """ `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
                    f""" {pretrained_model_name_or_path} automatically truncating your input to"""
                    f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
                    f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
                    """ `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
                    """ instantiate this tokenizer with `model_max_length` set to your preferred value."""  ,       __UpperCamelCase  ,       )

        return max_model_length







    def   _snake_case       (      self      :							Tuple  ,       __UpperCamelCase      :							str  ,       __UpperCamelCase      :							Optional[str] = None  )		->Tuple[str]:






        '''simple docstring'''






        if not self.can_save_slow_tokenizer:
            raise ValueError(
                """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                """tokenizer."""  )

        if not os.path.isdir(__UpperCamelCase  ):
            logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
            return
        _UpperCAmelCase        = os.path.join(
            __UpperCamelCase  ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

        if os.path.abspath(self.vocab_file  ) != os.path.abspath(__UpperCamelCase  ):
            copyfile(self.vocab_file  ,       __UpperCamelCase  )
            logger.info(f"""Copy vocab file to {out_vocab_file}"""  )

        return (out_vocab_file,)







    def   _snake_case       (      self      :							Tuple  ,       __UpperCamelCase      :							List[int]  ,       __UpperCamelCase      :							Optional[List[int]] = None  )		->List[int]:






        '''simple docstring'''






        _UpperCAmelCase        = token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
            return self.prefix_tokens + token_ids_a
        else:
            _UpperCAmelCase        = token_ids_a + [self.eos_token_id]
            return self.prefix_tokens + token_ids_a + token_ids_a







    def   _snake_case       (      self      :							Optional[Any]  ,       __UpperCamelCase      :							List[int]  ,       __UpperCamelCase      :							Optional[List[int]] = None  )		->List[int]:






        '''simple docstring'''






        _UpperCAmelCase        = [self.eos_token_id]

        if token_ids_a is None:
            return len(token_ids_a + eos  ) * [0]
        return len(token_ids_a + eos + token_ids_a + eos  ) * [0]







    def   _snake_case       (      self      :							Any  )		->List[str]:






        '''simple docstring'''






        return list(
            set(filter(lambda __UpperCamelCase  : bool(re.search(r"""<extra_id_\d+>"""  ,       __UpperCamelCase  )  ) is not None  ,       self.additional_special_tokens  )  )  )







    def   _snake_case       (      self      :							List[Any]  )		->Tuple:






        '''simple docstring'''






        return [self.convert_tokens_to_ids(__UpperCamelCase  ) for token in self.get_sentinel_tokens()]

"""simple docstring"""



import math
import unittest


def 			_UpperCamelCase						(    _A   )     ->	bool:





    """simple docstring"""

    assert isinstance(_A       ,   _A   ) and (
        number >= 0
    ), "'number' must been an int and positive"

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5       ,   int(math.sqrt(_A   ) + 1   )       ,   6   ):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True




class 		a_    (			unittest.TestCase						):







    def   _snake_case       (      self      :							int  )		->Optional[int]:






        '''simple docstring'''






        self.assertTrue(is_prime(2  )  )
        self.assertTrue(is_prime(3  )  )
        self.assertTrue(is_prime(5  )  )
        self.assertTrue(is_prime(7  )  )
        self.assertTrue(is_prime(11  )  )
        self.assertTrue(is_prime(13  )  )
        self.assertTrue(is_prime(17  )  )
        self.assertTrue(is_prime(19  )  )
        self.assertTrue(is_prime(23  )  )
        self.assertTrue(is_prime(29  )  )







    def   _snake_case       (      self      :							Union[str, Any]  )		->Optional[Any]:






        '''simple docstring'''






        with self.assertRaises(__UpperCamelCase  ):
            is_prime(-19  )
        self.assertFalse(
            is_prime(0  )  ,       """Zero doesn't have any positive factors, primes must have exactly two."""  ,       )
        self.assertFalse(
            is_prime(1  )  ,       """One only has 1 positive factor, primes must have exactly two."""  ,       )
        self.assertFalse(is_prime(2 * 2  )  )
        self.assertFalse(is_prime(2 * 3  )  )
        self.assertFalse(is_prime(3 * 3  )  )
        self.assertFalse(is_prime(3 * 5  )  )
        self.assertFalse(is_prime(3 * 5 * 7  )  )


if __name__ == "__main__":
 unittest.main()